Machine Learning Methods for Demand Estimation

We survey and apply several techniques from the statistical and computer science literature to the problem of demand estimation. To improve out-of-sample prediction accuracy, we propose a method of combining the underlying models via linear regression. Our method is robust to a large number of regre...

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Bibliographic Details
Published in:	The American economic review 2015-05, Vol.105 (5), p.481-485
Main Authors:	Bajari, Patrick, Nekipelov, Denis, Ryan, Stephen P., Yang, Miaoyu
Format:	Article
Language:	eng
Subjects:	Applied econometrics Applied economics Artificial intelligence Computer science Datasets Economic modeling Economic models Economic statistics Economic theory Economics Estimating techniques Estimation methods Learning Machine learning MACHINE LEARNING METHODS IN ECONOMICS AND ECONOMETRICS Modeling Open source software Regression analysis Scanners Software packages Studies Support vector machines Trees Variables
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Summary:	We survey and apply several techniques from the statistical and computer science literature to the problem of demand estimation. To improve out-of-sample prediction accuracy, we propose a method of combining the underlying models via linear regression. Our method is robust to a large number of regressors; scales easily to very large data sets; combines model selection and estimation; and can flexibly approximate arbitrary non-linear functions. We illustrate our method using a standard scanner panel data set and find that our estimates are considerably more accurate in out-of-sample predictions of demand than some commonly used alternatives.
ISSN:	0002-8282 1944-7981